Introduction
Insect pests have a great impact on many aspects of human life. Among all of these aspects, the harm to human health and the yield loss in agricultural production are the most concerning. To make matters worse, some insects serve as medium of pathogens, spreading diseases and causing damage simultaneously. For example, Anopheles gambiaespread malaria and caused millions of deaths annually in Africa (Consortium, 2017). As for agricultural production, the estimated yield loss of crops due to insect pests is over 18% globally (Oerke, 2005).
Although there are many insect pest control methods available, application of insecticides is still one of the most frequently used method. Chemical insecticides were first introduced to controlinsect pests in the 1940s. Since then, thousands of insecticides have been developed to protect human health and crops. Unfortunately, long-term mismanagement of insecticide application led to the development of insecticide resistance within insect pestpopulations. So far, more than 553 insect species have been reported to have developed resistance to approximately 331 insecticides (Gould, Brown, & Kuzma, 2018). The development of insecticide resistance necessitates the application of higher dosages of said insecticide for controlling insect pests , which in turn causes more serious threats to human and environmental health (Kim, Kabir, & Jahan, 2017; Tang et al., 2018). Insecticide resistance has become one of the most formidable obstacles in insect pest control (Gould, Brown, & Kuzma, 2018).
Insecticide target-site insensitive mutations and overexpression of detoxification gene(s) are two major mechanisms conferring insecticide resistance (Ffrench-Constant, 2013). Due to long-term selection by insecticides, the individuals containing resistance associated genotypes rapidly accumulate within populations. Generally, insecticide resistance of insect pest populations can be predicted according to the prevalence of target insensitive mutations and overexpression of detoxification genes (Sonoda, 2010). To date, most resistance cases occurred within five classes of insecticides: organophosphates, pyrethroids, carbamates, neonicotinoids and diamides (Thomas & Ralf, 2015). According to the modes of action listed by the Insecticide Resistance Action Committee (IRAC), organophosphates and carbamates target acetylcholinesterases (AChE), pyrethroids target voltage gated sodium channels (VGSC), diamides target ryanodine receptors (RyR), and neonicotinoids target nicotinic acetylcholine receptor (nAChR). In addition, metabolic resistances of these five classes of insecticides are mainly associated with three important detoxification gene families: cytochrome P450 (P450), glutathione S-transferase (GST) and carboxyl/cholinesterases (CCE) (L. Yan et al., 2012).
Detecting the target insensitive mutations and overexpressed detoxification genes within an insect pest population has long been a useful method in monitoring resistance. Many methods have been developed to detect target mutations such as PCR amplification of specific alleles (PASA) (H. H. Yan et al., 2014) and random amplified polymorphic DNA (RAPD) (Ferguson & Pineda, 2010). DNA microarray has been used detecting overexpressed detoxification genes (Mavridis et al., 2019). However, these methods are inefficient and time-consuming.
RNA-Seq data contains information allowing detection of single nucleotide polymorphisms (SNPs) and gene expression levels (Costa, Angelini, De Feis, & Ciccodicola, 2010). Thus, RNA-Seq data can be used to detect target-site insensitive mutations and overexpressed detoxification genes (Bonizzoni et al., 2015; De Wit, Pespeni, & Palumbi, 2015). Here,to monitor the resistance of the aforementioned five classes of insecticides, we collected reported target insensitive mutations, target gene allelic sequences, three groups of detoxification gene sequences from 82 insect species, and then developed a program, FastD, to detect target insensitive mutations and overexpressed detoxification genes from RNA-Seq data. To validate the reliability, we applied FastD to detect target-site mutations and overexpressed detoxification genes in five populations of two notorious insect pest species, P. xylostella and A. gossypii .